Many power applications require a well-designed battery management system (BMS) for operational safety and performance. The BMS monitors a current status of a battery, and regulates charging and discharging processes. One fundamental function is to estimate a state-of-charge (SoC) of the battery, i.e., a ratio of the current battery capacity over the maximal capacity.
In SoC estimation, one notable trend is an increasing emphasis on model-based estimation methods. While battery modeling is well known, more attention is being directed to the development of estimation methods.
Because a good model is a prerequisite, model-based SoC estimation typically uses dynamic modeling and parameter identification. However, accurate identification is difficult for the following reasons. The parameters for a battery model change over time and with varying operational conditions. The internal resistance increases and the capacity decreases as a result of battery aging. The charging and discharging efficiencies are dependent on the SoC and the current and temperature. The parameters can differ from one battery to another, making identification for each battery difficult. Therefore, adaptive methods are preferred. Adaptive methods perform identification and SoC estimation jointly.
As shown in FIG. 1, an adaptive SoC estimator provides the SoC 140 and estimates of the model parameters in real time after assimilating the current-voltage data on the basis of a model 110. The parameter estimates 111 are used to update the model to assist the estimation 120. A battery 100 is connected to a voltage sensor 102, a current sensor 102, and a temperature sensor 103. The parameter estimation unit 120 can, for example, use a UD-recursive least square (RLS) to estimate parameters for the model based on an battery equivalent circuit model 121.
One adaptive Extended Kalman Filter (EKF) based SoC estimator interacts with a parameter estimator. In another method, state augmentation is performed to incorporate the SoC variable and model parameters, and then an unscented Kalman Filter (UKF) is applied to estimate an augmented state. However, the convergence, and as a result, the accuracy, are difficult to guarantee. In another method, an adaptive SoC estimator is developed using an Iterative Extended Kalman Filter (IEKF), guided by an analysis of the observability and identifiability. An adaptive Partial Differential Equations (PDE) observer for SoC estimation is also known. It should be noted that all conventional methods are based on a single battery model.
The related Application uses an adaptive approach for SoC estimation via IEKF based simultaneous state and parameter estimation. While credible estimation is obtained, the accuracy can still limited due to a mismatch between the model and the actual system.